The present invention relates to a method of characterizing the coherence of measurements of characteristics of a medium and, more particularly, of logging data, said method making it possible, in particular, to characterize the relationships between, on the one hand, a qualitative variable whose origin is not logging-related and, on the other hand, logging measurements.
An oil field may, for example, comprise a number of wells which may be close together or far apart.
When studying such a field, geologists generally have available only a small number of core samples taken from one or more wells, which are then considered as being reference wells, the various measurements made on the core samples being considered as being reference measurements. On zones containing wells which are referred to as application wells and from which no core sample has been taken, specialists use other means for studying them, such as logging, seismic, etc. The data provided by these other means make it possible to determine petro-physical or other parameters and, in particular, to check their coherence with the measurements made on the available core samples. Checking and/or extrapolating data which are not related to core samples make it possible to characterize both quantitative variables and qualitative variables.
A geologist or another specialist, such as a sedimentologist, divides the zone from which core samples are available into a certain number of slices representative of various qualitative variables such as facies, sedimentary body, sedimentary environment, etc. Each of these variables is generally assigned a number.
For example, clay facies is referenced by the number 1, sand facies by the number 2, sandstone facies by the number 3, and so on. In this way, a qualitative variable is obtained which is referenced in depth and contains whole values, each value corresponding to one class of one logging viewpoint.
It should be noted that the measurements or descriptions on core samples may greatly exceed the resolution of conventional logging tools as used currently and, in particular, by the company SCHLUMBERGER. However, these descriptions are sometimes visual, and therefore subjective because they depend directly on the quality of observation by the geologist or the sedimentologist. It is not easy to obtain a comparison or a parallel between logging measurements made on one or more application wells because numerous "mixing" zones are often observed, that is to say the logging measurements may belong to several classes.
In order to reduce the specialist's subjectivity as far as possible, the use of statistical or neural classification methods has been advocated. Conventional statistical methods, such as those developed by TETZLAFF et al. at SPWLA in 1989, JIAN et al. in "Journal of Petroleum Geology, V. 17, January, p. 71-88, or GREDER et al. at SPWLA in 1995, give poor results when the classes overlap too much, because these so-called parametric methods assume that each class follows a Gaussian law. However, the shape of a class is generally more complex than a simple Gauss curve.
Neural classification methods, described in particular by CARDON et al. in 1991, ROGERS et al. in AAPG Bulletin 1992, V. 76, No. 5 p. 38-49, HALL et al. at SPWLA in 1995 or MOHAGHEGH in 1995 and 1996, also give poor results owing to the fact that the methods are highly sensitive to incoherences observed in the measurements. In neural networks whose learning is supervised, the network learns to recognize one shape from examples. However, a logging measurement may be attributed to one class in a first step, then be attributed to another class in another step. Neural networks with overlaid levels do not succeed in recognising that a single measurement is involved.
Reference was made above to core samples which are taken from one or more reference wells and on the basis of which classifications have been made using the methods summarized above. It should, however, be noted that the methods have also been applied to logging measurements produced directly in one reference well or several reference wells, for which the logging measurements have been considered as satisfactory because of their accuracy or since they were representative of qualitative variables of the well or wells. It is nonetheless true that the results obtained with the methods on reference logging measurements were unsatisfactory and suffered from the same drawbacks. Specifically, these methods give poor results when a facies not described in the learning set is encountered in an application well and/or when the quality of the logging measurements is compromised by defective calibration or corrections, or on account of poor acquisition conditions.